Overview
Autonome supports multiple AI providers (OpenRouter, Nvidia NIM, AIHubMix) and strategy variants that implement distinct trading philosophies. Each variant has a custom system prompt and user prompt that shape the agent’s decision-making.Apex
Geometric Growth Engine
High leverage (10x), volatility squeezes, profit ratcheting
High leverage (10x), volatility squeezes, profit ratcheting
Trendsurfer
Momentum Rider
ADX filtering, Ichimoku cloud breakouts, trailing stops
ADX filtering, Ichimoku cloud breakouts, trailing stops
Contrarian
Mean Reversion Specialist
RSI extremes, oversold bounces, fixed targets
RSI extremes, oversold bounces, fixed targets
Sovereign
Risk-Adjusted Strategist
Kelly Criterion sizing, macro confluence, structured exits
Kelly Criterion sizing, macro confluence, structured exits
Variant Configuration
Variants are defined in the shared configuration module:AI Provider Setup
Autonome integrates three AI providers with API key rotation:- Define multiple API keys in
.env.local:NIM_API_KEY_1,NIM_API_KEY_2, … getNextNimApiKey()cycles through keys using a round-robin counter- Prevents rate limits when running multiple parallel agents
src/env.ts for rotator implementation.
Prompt Architecture
Each variant has two prompts:- System Prompt: Static strategy rules, identity, tool usage guidelines
- User Prompt: Dynamic template with placeholders for market data and portfolio state
Example: Apex (High Leverage Aggressor)
Example: Trendsurfer (Momentum Rider)
Trendsurfer uses no fixed profit targets—only trailing stops via
updateExitPlan. This prevents premature exits in strong trends.Data Source Hierarchy
All prompts enforce a critical data hierarchy:- Manual/Exchange Indicators (Execution): Use for exact entry price, stop loss, and invalidation (orderbook-based)
- Taapi/Binance Indicators (Context): Use for broad trend and market regime (ADX, Supertrend, Ichimoku)
Prompt Data Principles
Autonome follows strict spoon-feeding guidelines when building prompts:Principle 1: Explicit Labels
Bad:risk $128.56 (ambiguous: USD or basis points?)Good:
risk_usd $128.56 (unambiguous)
Principle 2: Show Zeros
Bad: Omitscaled_realized if zeroGood:
scaled_realized $0.00 (indicates no partial closes yet)
Principle 3: Omit N/A
Bad:funding_rate N/A (noise)Good: Only show funding_rate if data exists
Principle 4: No Duplication
Each metric lives in exactly one section:- Session Header:
invocationCount,currentTime,availableCash,exposurePct - PORTFOLIO:
totalValue,unrealizedPnl,leverage,riskUsd - PERFORMANCE:
sharpeRatio,winRate,maxDrawdown,closedTradeRealizedPnl - OPEN POSITIONS: Per-position
entryPrice,markPrice,unrealizedPnl,roe
Principle 5: Clarity Over Brevity
Bad:SR (token-optimized)Good:
sharpe_ratio (explicit, no ambiguity)
Prompt Builder Implementation
Prompt Sections
Prompts are assembled from modular sections:Consensus Orchestrator
For enhanced decision quality, Autonome supports parallel consensus voting across multiple models:- Run 3+ models in parallel with same market data
- Aggregate decisions via weighted voting
- Only execute trades where 2/3+ models agree with confidence >= 6/10
- Reduces single-model bias
- Higher confidence trades
- Exploits diverse reasoning styles
src/server/features/trading/consensusOrchestrator.ts for full implementation.
Model Configuration
Related Resources
Autonomous Trading Loop
Learn how agents execute the full trading workflow
Configuration
Set up API keys and model providers
Strategy Variants
Explore the different AI trading strategies
Trading System
Understand the trading system architecture

